qem.utils

Functions

`apply_threshold`(image, image_ref, threshold)
`broadcast_from_unmeshed`(coords)	For an unmeshed set of coordinates broadcast to a meshed ND array.
`export_params`(params, filename)	Export the parameters to a file.
`fft2d`(array)	2D FFT of a numpy array.
`find_duplicate_row_indices`(array)	Find the indices of duplicate rows in a NumPy array.
`find_element_indices`(array1, array2)	Find indices of elements of array1 in array2.
`find_peaks_in_rectangle`(peaks, origin, a, b)	Find all peaks that lie within the rectangle defined by origin, origin+a, origin+b, and origin+a+b.
`find_row_indices`(array1, array2)	Efficiently find the indices of rows of array1 in array2.
`get_random_indices_in_batches`(...)
`ifft2d`(array)	2D inverse FFT of a numpy array.
`is_point_in_polygon`(point, polygon)	Determine if a point is inside a polygon using the ray casting algorithm.
`plot_image`(image, x_labels, y_labels[, ...])	Plot an image.
`q_space_array`(pixels, gridsize[, meshed])	Return the appropriately scaled 2D reciprocal space coordinates.
`remove_close_coordinates`(coordinates, threshold)	Remove coordinates that are within a specified threshold distance of each other.
`remove_freq`(image, low, high)
`rotate_vector`(vector, axis, angle)
`safe_convert_to_numpy`(tensor)	Safely convert a Keras tensor to numpy array, handling different backends.
`safe_convert_to_tensor`(array[, dtype])	Safely convert a numpy array to Keras tensor.
`safe_deepcopy_params`(params)	Safely deep copy a parameter dictionary containing tensors.
`safe_ln`(x)	Natural logarithm function, avoiding division by zero warnings.

qem.utils.plot_image(image, x_labels, y_labels, colormap='gray', colorbar=True)[source]

Plot an image. with x and y labels.

Parameters:

image (numpy.ndarray) – The image to plot.
x_labels (list) – The labels for the x-axis.
y_labels (list) – The labels for the y-axis.
colormap (str, optional) – The colormap to use.
colorbar (bool, optional) – Whether to show a colorbar.

Returns:

matplotlib.pyplot.figure – The figure containing the plot.

qem.utils.safe_ln(x)[source]

Natural logarithm function, avoiding division by zero warnings.

Parameters:: x (float) – The value to take the logarithm of.
Returns:: float – The natural logarithm of x.

qem.utils.fft2d(array)[source]

2D FFT of a numpy array.

Parameters:: array (numpy.ndarray) – The array to transform.
Returns:: numpy.ndarray – The transformed array.

qem.utils.ifft2d(array)[source]

2D inverse FFT of a numpy array.

Parameters:: array (numpy.ndarray) – The array to transform.
Returns:: numpy.ndarray – The transformed array.

qem.utils.remove_freq(image, low, high)[source]

qem.utils.apply_threshold(image, image_ref, threshold)[source]

qem.utils.make_mask_circle_centre(arr, radius)

Create a circular mask with same shape as arr

The circle is centered on the center of the array, with the circle having True values.

Similar to _make_circular_mask, but simpler and potentially faster.

Numba jit compatible.

Parameters:

arr (NumPy array) – Must be 2 dimensions
radius (scalar) – Radius of the circle

Returns:

mask (NumPy array) – Boolean array

Example

>>> import atomap.atom_finding_refining as afr
>>> arr = np.random.randint(100, size=(20, 20))
>>> mask = afr._make_mask_circle_centre(arr, 10)

qem.utils.find_duplicate_row_indices(array)[source]

Find the indices of duplicate rows in a NumPy array.

Parameters: - array: NumPy array to check for duplicate rows.

Returns: - idx_duplicates: NumPy array of indices of the duplicate rows.

qem.utils.find_row_indices(array1, array2)[source]

Efficiently find the indices of rows of array1 in array2.

Parameters:

array1 – A NumPy array of shape (M, N).
array2 – A NumPy array of shape (K, N).

Returns:

A NumPy array of indices indicating the position of each row of array1 in array2. If a row from array1 is not found in array2, the corresponding index is -1.

qem.utils.find_element_indices(array1, array2)[source]

Find indices of elements of array1 in array2.

Parameters:

array1 – A 1D NumPy array of elements to find.
array2 – A 1D NumPy array where to search for the elements.

Returns:

A list of indices indicating the position of each element of array1 in array2. If an element from array1 is not found in array2, the corresponding index is -1.

qem.utils.remove_close_coordinates(coordinates, threshold)[source]

Remove coordinates that are within a specified threshold distance of each other.

Parameters: - threshold: The distance below which coordinates are considered too close and should be removed.

qem.utils.export_params(params, filename)[source]

Export the parameters to a file.

Parameters: - params: Dictionary of parameters to export. - filename: Name of the file to export to.

qem.utils.get_random_indices_in_batches(total_examples, batch_size)[source]

qem.utils.is_point_in_polygon(point, polygon)[source]

Determine if a point is inside a polygon using the ray casting algorithm.

Parameters: - point: A 2D point as a tuple or numpy array (x, y). - polygon: A list of tuples or numpy arrays [(x1, y1), (x2, y2), …, (xn, yn)] representing the polygon vertices.

Returns: - Boolean indicating whether the point is inside the polygon.

qem.utils.find_peaks_in_rectangle(peaks, origin, a, b)[source]

Find all peaks that lie within the rectangle defined by origin, origin+a, origin+b, and origin+a+b.

Parameters: - peaks: A list of peak positions as tuples or numpy arrays (x, y). - origin: The origin point as a tuple or numpy array (x, y). - a: The vector a as a tuple or numpy array (x, y). - b: The vector b as a tuple or numpy array (x, y).

Returns: - A list of peaks within the defined rectangle.

qem.utils.rotate_vector(vector, axis, angle)[source]

qem.utils.q_space_array(pixels, gridsize, meshed=True)[source]

Return the appropriately scaled 2D reciprocal space coordinates.

Parameters:

pixels ((N,) array_like) – Pixels in each dimension of a ND array
gridsize ((N,) array_like) – Dimensions of the array in real space units
meshed (bool, optional) – Option to output dense meshed grid (True) or output unbroadcasted arrays (False)
pixels – Pixels in each dimension of a 2D array
gridsize – Dimensions of the array in real space units

qem.utils.broadcast_from_unmeshed(coords)[source]

For an unmeshed set of coordinates broadcast to a meshed ND array.

Examples

>>> broadcast_from_unmeshed([np.arange(5),np.arange(6)])
[array([[0, 0, 0, 0, 0, 0],
   [1, 1, 1, 1, 1, 1],
   [2, 2, 2, 2, 2, 2],
   [3, 3, 3, 3, 3, 3],
   [4, 4, 4, 4, 4, 4]]), array([[0, 1, 2, 3, 4, 5],
   [0, 1, 2, 3, 4, 5],
   [0, 1, 2, 3, 4, 5],
   [0, 1, 2, 3, 4, 5],
   [0, 1, 2, 3, 4, 5]])]

qem.utils.safe_convert_to_numpy(tensor)[source]

Safely convert a Keras tensor to numpy array, handling different backends.

Parameters:: tensor – Keras tensor or numpy array
Returns:: numpy.ndarray – The tensor converted to numpy array

qem.utils.safe_convert_to_tensor(array, dtype='float32')[source]

Safely convert a numpy array to Keras tensor.

Parameters:

array – numpy array or tensor
dtype – target dtype for the tensor

Returns:

Keras tensor

qem.utils.safe_deepcopy_params(params)[source]

Safely deep copy a parameter dictionary containing tensors.

Parameters:: params – Dictionary containing tensors and other values
Returns:: Dictionary with safely copied parameters